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arXiv 2008.03046cs.SE
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用概率模型设计具有内在不确定性的软件系统

Towards Using Probabilistic Models to Design Software Systems with Inherent Uncertainty

Alex Serban, Erik Poll, Joost Visser

摘要:

在软件系统中采用机器学习(ML)组件提出了新的工程挑战。特别是,固有的不确定性,功能的适用性和操作环境,使体系结构的评估和权衡分析困难。我们提出了一种称为设计过程中建模不确定性(MUDD)的软件架构评估方法,该方法显式地对与ML组件相关的不确定性进行建模,并评估它如何通过系统传播。该方法支持对架构模式如何减轻不确定性的推理,并能够比较不同的架构,重点关注ML和经典软件组件之间的相互作用。虽然我们的方法是领域不可知的,适用于任何不确定性起核心作用的系统,但我们以自动驾驶的感知系统为例展示了我们的方法。

英文摘要:

The adoption of machine learning (ML) components in software systems raises new engineering challenges. In particular, the inherent uncertainty regarding functional suitability and the operation environment makes architecture evaluation and trade-off analysis difficult. We propose a software architecture evaluation method called Modeling Uncertainty During Design (MUDD) that explicitly models the uncertainty associated to ML components and evaluates how it propagates through a system. The method supports reasoning over how architectural patterns can mitigate uncertainty and enables comparison of different architectures focused on the interplay between ML and classical software components. While our approach is domain-agnostic and suitable for any system where uncertainty plays a central role, we demonstrate our approach using as example a perception system for autonomous driving.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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